UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

Code

• github.com/lmcinnes/umap
  OfficialIn papertf★ 8,125
• github.com/jlmelville/uwot
  OfficialIn papernone★ 356
• github.com/mdozmorov/scRNA-seq_notes
  tf★ 780
• github.com/bmolab/masked-gan-manifold
  pytorch★ 159
• github.com/tkonopka/umap
  none★ 0
• github.com/tag-bio/umap-java
  none★ 0
• github.com/ropensci-archive/umapr
  none★ 0
• github.com/mevers/animated_dimensionality_reduction
  none★ 0
• github.com/emnh/opengameart
  tf★ 0
• github.com/tjburns08/umap-for-cytof
  none★ 0

Abstract

UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.

Tasks

Benchmark Results

Reproductions